
import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						(CMStochasticIterativeScheduler,)
  a__       			=						10
  def 				_lowercase				(					self     :					Any     ,				**UpperCamelCase__     :					List[str]       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			{
        """num_train_timesteps""": 201,
        """sigma_min""": 0.002,
        """sigma_max""": 80.0,
    }

    config.update(**UpperCamelCase__       )
    return config
  def 				_lowercase				(					self     :					List[Any]       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			10

    __magic_name__								=			self.get_scheduler_config()
    __magic_name__								=			self.scheduler_classes[0](**UpperCamelCase__       )

    scheduler.set_timesteps(UpperCamelCase__       )

    __magic_name__								=			scheduler.timesteps[0]
    __magic_name__								=			scheduler.timesteps[1]

    __magic_name__								=			self.dummy_sample
    __magic_name__								=			0.1 * sample

    __magic_name__								=			scheduler.step(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       ).prev_sample
    __magic_name__								=			scheduler.step(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       ).prev_sample

    self.assertEqual(output_a.shape     ,				sample.shape       )
    self.assertEqual(output_a.shape     ,				output_a.shape       )
  def 				_lowercase				(					self     :					Dict       )     ->      Tuple:







    """simple docstring"""






    for timesteps in [10, 50, 100, 1000]:
      self.check_over_configs(num_train_timesteps=UpperCamelCase__       )
  def 				_lowercase				(					self     :					List[Any]       )     ->      str:







    """simple docstring"""






    for clip_denoised in [True, False]:
      self.check_over_configs(clip_denoised=UpperCamelCase__       )
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			self.scheduler_classes[0]
    __magic_name__								=			self.get_scheduler_config()
    __magic_name__								=			scheduler_class(**UpperCamelCase__       )

    __magic_name__								=			1
    scheduler.set_timesteps(UpperCamelCase__       )
    __magic_name__								=			scheduler.timesteps

    __magic_name__								=			torch.manual_seed(0       )

    __magic_name__								=			self.dummy_model()
    __magic_name__								=			self.dummy_sample_deter * scheduler.init_noise_sigma

    for i, t in enumerate(UpperCamelCase__       ):
      # 1. scale model input
      __magic_name__								=			scheduler.scale_model_input(UpperCamelCase__     ,				UpperCamelCase__       )

      # 2. predict noise residual
      __magic_name__								=			model(UpperCamelCase__     ,				UpperCamelCase__       )

      # 3. predict previous sample x_t-1
      __magic_name__								=			scheduler.step(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				generator=UpperCamelCase__       ).prev_sample

      __magic_name__								=			pred_prev_sample

    __magic_name__								=			torch.sum(torch.abs(UpperCamelCase__       )       )
    __magic_name__								=			torch.mean(torch.abs(UpperCamelCase__       )       )

    assert abs(result_sum.item() - 192.7614       ) < 1E-2
    assert abs(result_mean.item() - 0.2510       ) < 1E-3
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			self.scheduler_classes[0]
    __magic_name__								=			self.get_scheduler_config()
    __magic_name__								=			scheduler_class(**UpperCamelCase__       )

    __magic_name__								=			[106, 0]
    scheduler.set_timesteps(timesteps=UpperCamelCase__       )
    __magic_name__								=			scheduler.timesteps

    __magic_name__								=			torch.manual_seed(0       )

    __magic_name__								=			self.dummy_model()
    __magic_name__								=			self.dummy_sample_deter * scheduler.init_noise_sigma

    for t in timesteps:
      # 1. scale model input
      __magic_name__								=			scheduler.scale_model_input(UpperCamelCase__     ,				UpperCamelCase__       )

      # 2. predict noise residual
      __magic_name__								=			model(UpperCamelCase__     ,				UpperCamelCase__       )

      # 3. predict previous sample x_t-1
      __magic_name__								=			scheduler.step(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				generator=UpperCamelCase__       ).prev_sample

      __magic_name__								=			pred_prev_sample

    __magic_name__								=			torch.sum(torch.abs(UpperCamelCase__       )       )
    __magic_name__								=			torch.mean(torch.abs(UpperCamelCase__       )       )

    assert abs(result_sum.item() - 347.6357       ) < 1E-2
    assert abs(result_mean.item() - 0.4527       ) < 1E-3
  def 				_lowercase				(					self     :					Tuple       )     ->      str:







    """simple docstring"""






    __magic_name__								=			self.scheduler_classes[0]
    __magic_name__								=			self.get_scheduler_config()
    __magic_name__								=			scheduler_class(**UpperCamelCase__       )

    __magic_name__								=			[39, 30, 12, 15, 0]

    with self.assertRaises(UpperCamelCase__     ,				msg="""`timesteps` must be in descending order."""       ):
      scheduler.set_timesteps(timesteps=UpperCamelCase__       )
  def 				_lowercase				(					self     :					Optional[int]       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			self.scheduler_classes[0]
    __magic_name__								=			self.get_scheduler_config()
    __magic_name__								=			scheduler_class(**UpperCamelCase__       )

    __magic_name__								=			[39, 30, 12, 1, 0]
    __magic_name__								=			len(UpperCamelCase__       )

    with self.assertRaises(UpperCamelCase__     ,				msg="""Can only pass one of `num_inference_steps` or `timesteps`."""       ):
      scheduler.set_timesteps(num_inference_steps=UpperCamelCase__     ,				timesteps=UpperCamelCase__       )





  def 				_lowercase				(					self     :					List[Any]       )     ->      Optional[int]:







    """simple docstring"""






    __magic_name__								=			self.scheduler_classes[0]
    __magic_name__								=			self.get_scheduler_config()
    __magic_name__								=			scheduler_class(**UpperCamelCase__       )

    __magic_name__								=			[scheduler.config.num_train_timesteps]

    with self.assertRaises(
        UpperCamelCase__     ,				msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""     ,				):
      scheduler.set_timesteps(timesteps=UpperCamelCase__       )



from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						["""image_processor""", """tokenizer"""]
  a__       			=						"""BridgeTowerImageProcessor"""
  a__       			=						("""RobertaTokenizer""", """RobertaTokenizerFast""")
  def __init__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Any       )     ->      Optional[int]:







    """simple docstring"""






    super().__init__(UpperCamelCase__     ,				UpperCamelCase__       )
  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Union[bool, str, PaddingStrategy] = False     ,				UpperCamelCase__     :					Union[bool, str, TruncationStrategy] = None     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					int = 0     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Optional[Union[str, TensorType]] = None     ,				**UpperCamelCase__     :					Dict     ,				)     ->      BatchEncoding:







    """simple docstring"""






    __magic_name__								=			self.tokenizer(
        text=UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				max_length=UpperCamelCase__     ,				stride=UpperCamelCase__     ,				pad_to_multiple_of=UpperCamelCase__     ,				return_token_type_ids=UpperCamelCase__     ,				return_attention_mask=UpperCamelCase__     ,				return_overflowing_tokens=UpperCamelCase__     ,				return_special_tokens_mask=UpperCamelCase__     ,				return_offsets_mapping=UpperCamelCase__     ,				return_length=UpperCamelCase__     ,				verbose=UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				**UpperCamelCase__     ,				)
    # add pixel_values + pixel_mask
    __magic_name__								=			self.image_processor(
        UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				do_normalize=UpperCamelCase__     ,				do_center_crop=UpperCamelCase__     ,				**UpperCamelCase__       )
    encoding.update(UpperCamelCase__       )

    return encoding
  def 				_lowercase				(					self     :					Optional[Any]     ,				*UpperCamelCase__     :					Optional[Any]     ,				**UpperCamelCase__     :					str       )     ->      str:







    """simple docstring"""






    return self.tokenizer.batch_decode(*UpperCamelCase__     ,				**UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				*UpperCamelCase__     :					int     ,				**UpperCamelCase__     :					Optional[Any]       )     ->      str:







    """simple docstring"""






    return self.tokenizer.decode(*UpperCamelCase__     ,				**UpperCamelCase__       )





  @property
  def 				_lowercase				(					self     :					Tuple       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			self.tokenizer.model_input_names
    __magic_name__								=			self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )

